Minor Contribution of Endogenous GLP-1 and GLP-2 to Postprandial Lipemia in Obese Men
Bibliographic record
Abstract
CONTEXT: Glucose and lipids stimulate the gut-hormones glucagon-like peptide (GLP)-1, GLP-2 and glucose-dependent insulinotropic polypeptide (GIP) but the effect of these on human postprandial lipid metabolism is not fully clarified. OBJECTIVE: To explore the responses of GLP-1, GLP-2 and GIP after a fat-rich meal compared to the same responses after an oral glucose tolerance test (OGTT) and to investigate possible relationships between incretin response and triglyceride-rich lipoprotein (TRL) response to a fat-rich meal. DESIGN: Glucose, insulin, GLP-1, GLP-2 and GIP were measured after an OGTT and after a fat-rich meal in 65 healthy obese (BMI 26.5-40.2 kg/m(2)) male subjects. Triglycerides (TG), apoB48 and apoB100 in TG-rich lipoproteins (chylomicrons, VLDL1 and VLDL2) were measured after the fat-rich meal. MAIN OUTCOME MEASURES: Postprandial responses (area under the curve, AUC) for glucose, insulin, GLP-1, GLP-2, GIP in plasma, and TG, apoB48 and apoB100 in plasma and TG-rich lipoproteins. RESULTS: The GLP-1, GLP-2 and GIP responses after the fat-rich meal and after the OGTT correlated strongly (r = 0.73, p<0.0001; r = 0.46, p<0.001 and r = 0.69, p<0.001, respectively). Glucose and insulin AUCs were lower, but the AUCs for GLP-1, GLP-2 and GIP were significantly higher after the fat-rich meal than after the OGTT. The peak value for all hormones appeared at 120 minutes after the fat-rich meal, compared to 30 minutes after the OGTT. After the fat-rich meal, the AUCs for GLP-1, GLP-2 and GIP correlated significantly with plasma TG- and apoB48 AUCs but the contribution was very modest. CONCLUSIONS: In obese males, GLP-1, GLP-2 and GIP responses to a fat-rich meal are greater than following an OGTT. However, the most important explanatory variable for postprandial TG excursion was fasting triglycerides. The contribution of endogenous GLP-1, GLP-2 and GIP to explaining the variance in postprandial TG excursion was minor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".